Examination of blood samples using deep learning and mobile microscopy.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.

Authors

  • Juliane Pfeil
    Molecular Biology and Functional Genomics, Technical University of Applied Sciences, Hochschulring 1, 15745, Wildau, Germany.
  • Alina Nechyporenko
    Molecular Biology and Functional Genomics, Technical University of Applied Sciences, Hochschulring 1, 15745, Wildau, Germany.
  • Marcus Frohme
    Molecular Biology and Functional Genomics, Technical University of Applied Sciences, Hochschulring 1, 15745, Wildau, Germany. marcus.frohme@th-wildau.de.
  • Frank T Hufert
    Institute for Microbiology and Virology, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany.
  • Katja Schulze
    Oculyze GmbH, Mobile Microscopy and Computer Vision, Wildau, Germany.